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Semantic and secure search over encrypted outsourcing cloud based on BERT

Frontiers of computer science 2022年第2期16卷 152-159页

作者： Zhangjie FU Yan WANG Xingming SUN Xiaosong ZHANG Engineering Research Center of Digital Forensice Ministry of EducationNanjing University of Information Science and TechnologyNanjing 210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

Searchable encryption provides an effective way for data security and privacy in cloud *** can retrieve encrypted data in the cloud under the premise of protecting their own data security and ***,most of the current content-based retrieval schemes do not contain enough semantic information of the article and cannot fully reflect the semantic information of the *** this paper,we propose two secure and semantic retrieval schemes based on BERT(bidirectional encoder representations from transformers)named SSRB-1,*** training the documents with BERT,the keyword vector is generated to contain more semantic information of the documents,which improves the accuracy of retrieval and makes the retrieval result more consistent with the user’s ***,through testing on real data sets,it is shown that both of our solutions are feasible and effective.

关键词： cloud computing semantic search BERT model searchable encryption

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Multi-Optimiser Training for GANs Based on Evolutionary Computation 13

Multi-Optimiser Training for GANs Based on Evolutionary Comp...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Zhang, Yixia Xue, Yu Neri, Ferrante School of Software Nanjing University of Information Science and Technology Nanjing China School of Computer Science and Electronic Engineering University of Surrey Guildford United Kingdom

ISBN: (纸本)9798350308365

Generative adversarial networks (GANs) are widely recognized for their impressive ability to generate realistic data. Despite the popularity of GANs, training them poses challenges such as mode collapse and instability. To address these issues, many variants enhance GAN performance through improvements in network architecture, modifications to loss functions, and the inclusion of regularization techniques. However, a limited number of methods focuses on optimising GAN performance from the optimiser's perspective, despite the distinct roles different optimisers play in training. Existing GANs typically employ a single optimiser throughout, with Adam being the default choice for GAN training. Approaches using multiple optimisers have shown improved performance but are often task-specific. This paper introduces a novel, portable approach that lever-ages the strengths of multiple training methods, with an evolutionary supervisor coordinating the training of different sections of the network. Initially, real-number-encoded vectors representing the optimiser for each sub-parameter layer undergo pro-gressive enhancement using a standard evolutionary algorithm (SGA). Through the evolutionary process, optimal optimiser combinations are retained based on the performance of the trained GAN. The proposed method, SGA-GANs, is validated on the CIFAR10 dataset by integrating the steps into five benchmark GAN models: GAN, DCGAN, BEGAN, WGAN, and WGAN-GP. Experimental results demonstrate that SGA-GANs outperforms single optimiser training methods, achieving superior evaluation results and generating higher-quality images. Source code can be found at https://***/lizzhang-spec/SGA-GANs. © 2024 IEEE.

关键词： Generative adversarial networks

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CKKS and Local Differential Privacy based Model Parameters Encryption for Personalized Federated Learning 6

CKKS and Local Differential Privacy based Model Parameters E...

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6th International Conference on System Reliability and Safety Engineering, SRSE 2024

作者： Shen, Fanfan Hui, Lijie Liang, Qiwei Zhang, Jun Xu, Chao Chen, Yong He, Yanxiang Nanjing Audit University School of Computer Science Nanjing211815 China East China University of Science and Technology School of Software Nanchang330013 China Wuhan University School of Computer Science Wuhan430072 China

ISBN: (纸本)9798350356083

Federated learning achieves a certain degree of privacy protection by only uploading model parameters instead of the clients' raw data. However, attackers can still analyze model parameters to obtain sensitive data, and previous studies failed to fully balance the implementation of privacy protection and personalized requirements in federated learning. Therefore, we propose a Cheon-Kim-Kim-Song (CKKS) and Local Differential Privacy based Model Parameters Encryption for Personalized Federated Learning scheme to solve this problem, called CLDPMPE, which consists of two parts: model parameters privacy protection and model personalization. Our scheme ensures the security of model parameters by leveraging the CKKS encryption algorithm in conjunction with local differential privacy techniques. Furthermore, it facilitates model personalization through the application of meta-learning and dynamic clustering methodologies. Since then, while ensuring privacy and security, maintaining the model's generalization and personalization capabilities. Formal security analysis confirms that CLDP-MPE is secure and comprehensive experiments demonstrate that it achieves a high level of personalized testing precision (averaging 89.58%) while protecting privacy. © 2024 IEEE.

关键词： Federated learning

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Deep Combination of CDCl(T) and Local Search for Satisfiability Modulo Non-Linear Integer Arithmetic Theory 24

Deep Combination of CDCl(T) and Local Search for Satisfiabil...

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44th ACM/IEEE International Conference on software Engineering, ICSE 2024

作者： Zhang, Xindi Li, Bohan Cai, Shaowei Institute of Software Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences State Key Laboratory of Computer Science Beijing China

ISBN: (纸本)9798400702174

Satisfiability Modulo Theory (SMT) generalizes the propositional satisfiability problem (SAT) by extending support for various first-order background theories. In this paper, we focus on the SMT problems in Non-Linear Integer Arithmetic (NIA) theory, referred to as SMT(NIA), which has wide applications in software engineering. The dominant paradigm for SMT(NIA) is the CDCL(T) framework, while recently stochastic local search (SLS) has also shown its effectiveness. However, the cooperation between the two methods has not been studied yet. Motivated by the great success of the deep cooperation of CDCL and SLS for SAT, we propose a two-layer hybrid approach for SMT(NIA). The outer-layer interleaves between the inner-layer and an independent SLS solver. In the inner-layer, we take CDCL(T) as the main body, and design DCL(T)-guided SLS solver, which is invoked at branches corresponding to skeleton solutions and returns useful information to improve the branching heuristics of CDCL(T). We implement our ideas on top of the CDCL(T) tactic of Z3 with an SLS solver called Localsmt, resulting in a hybrid solver dubbed Hv-BRIDSMT. Extensive experiments are carried out on the standard SMT(NIA) benchmarks from SMT-LIB, where most of the instances are from real-world software engineering applications of termination and non-termination analysis. Experiment results show that Hybridsmt significantly improves the CDCL(T) solver in Z3. Moreover, our solver can solve 10.36% more instances than the currently best SMT(NIA) solver, and is more efficient for software verification instances. © 2024 ACM.

关键词： Stochastic systems

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Real-Time DAG Task Allocation Strategy for Multiprocessor by Optimistic Parallelism 24

Real-Time DAG Task Allocation Strategy for Multiprocessor by...

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24th IEEE International Conference on Communication technology, ICCT 2024

作者： Chen, Yumeng Liu, Songlin Zhou, Jianhong Ling, Xiang University of Electronic Science and Technology of China National Key Laboratory of Science and Technology on Communications China Xihua University School of Computer and Software Engineering China

ISBN: (纸本)9798350363760

In modern real-time operating systems, complex task loads are often modeled as directed acyclic graphs (DAG) and executed in parallel on multiprocessor systems. The topological constraints present in DAG tasks prevent the DAG task allocation strategy from fully leveraging the parallel advantage of multiprocessor platforms. We analyze the parallelism of DAG tasks and propose a method to limit the number of processors that can be executed by DAG tasks. This method provides more idle processors and processor time for high parallelism DAG tasks by limiting the number of processors that can be executed by low parallelism DAG tasks. In addition, we develope optimistic parallelism scheduling algorithm (OPSA) based on the remaining utilization with low complexity. The experimental results show that our DAG task allocation strategy fully utilizes processor resources, reduces interference between DAG tasks, and achieves better worst case response time (WCRT) analysis results and shorter actual task's WCRT on real-time multiprocessor system. © 2024 IEEE.

关键词： Directed graphs

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Integrated Comprehensive Analysis Method for Education Quality with Federated Learning 5th

Integrated Comprehensive Analysis Method for Education Qual...

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5th International Conference on computer science and Educational Informatization, CSEI 2023

作者： Wang, Ruijin Chen, Tin Wang, Jinbo Lai, Jinshan Li, Jingwei Zhang, Mengjie Chen, Xuxia School of information and software engineering University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Engineering University and Technology of china Chengdu611731 China

ISBN: (纸本)9789819994915

A thorough evaluation of education quality can help schools, governments, and related organizations evaluate education quality, detect persistent problems, act quickly, and raise teaching and educational standards. Traditional methods of assessing education quality, such as statistical analysis and teacher evaluations, suffer from limitations such as data fragmentation, privacy preservation and relying solely on one form of evaluation. We presented a method for comprehensively assessing education quality by incorporating federated learning (CEQFL) to fully utilize big data. First, under a unified evaluation model and indicators, universities act as terminal nodes to train the evaluation model using private data locally. Second, universities aggregate the model to the Ministry of Education for model aggregation and update the evaluation model parameters based on the aggregated data. Finally, using school data as experimental data, we demonstrate that CEQFL has strong education quality assessment capabilities. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality control

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Distributed Hierarchical Multi-Agent Reinforcement Learning for Real-World Adversarial Team Games 21

Distributed Hierarchical Multi-Agent Reinforcement Learning ...

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21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Wei, Li Bei, Hui Ke, Lu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

Real-world applications often require teams of agents to coordinate strategies against adversaries. However, existing MARL models overlook resource constraints and task complexity, limiting their practical use. To address this, we propose the Distributed Hierarchical Multi-agent Learning (DHML) framework, which uses distributed neural networks for efficient computation. A mean-field mechanism and an attentional communication module are further incorporated for scalability, and a hierarchical task decomposition is adopted based on the option-critic framework for handling complexity. Experiments on a simulation platform modeling real communication networks demonstrate DHML's effectiveness across various adversarial scenarios. © 2024 IEEE.

关键词： Adversarial machine learning

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A sharding blockchain-based UAV system for search and rescue missions

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Frontiers of computer science 2025年第3期19卷 103-118页

作者： Xihan ZHANG Jiashuo ZHANG Jianbo GAO Libin XIA Zhi GUAN Hao HU Zhong CHEN School of Computer Science Peking UniversityBeijing 100871China Peking University Chongqing Research Institute of Big Data Chongqing 401329China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China State Key Lab for Novel Software Technology Nanjing UniversityNanjing 210023China

Sharding is a promising technique to tackle the critical weakness of scalability in blockchain-based unmanned aerial vehicle(UAV)search and rescue(SAR)*** breaking up the blockchain network into smaller partitions called shards that run independently and in parallel,shardingbased UAV systems can support a large number of search and rescue UAVs with improved scalability,thereby enhancing the rescue ***,the lack of adaptability and interoperability still hinder the application of sharded blockchain in UAV SAR *** refers to making adjustments to the blockchain towards real-time surrounding situations,while interoperability refers to making cross-shard interactions at the mission *** address the above challenges,we propose a blockchain UAV system for SAR missions based on dynamic sharding *** from the benefits in scalability brought by sharding,our system improves adaptability by dynamically creating configurable and mission-exclusive shards,and improves interoperability by supporting calls between smart contracts that are deployed on different *** implement a prototype of our system based on Quorum,give an analysis of the improved adaptability and interoperability,and conduct experiments to evaluate the *** results show our system can achieve the above goals and overcome the weakness of blockchain-based UAV systems in SAR scenarios.

关键词： blockchain sharding unmanned aerial vehicle search and rescue blockchain interoperability

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Two-Stage Lesion Detection Approach Based on Dimension-Decomposition and 3D Context

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Tsinghua science and technology 2022年第1期27卷 103-113页

作者： Jiacheng Jiao Haiwei Pan Chunling Chen Tao Jin Yang Dong Jingyi Chen the College of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China the School of Software Tsinghua UniversityBeijng 100084China

Lesion detection in Computed Tomography(CT) images is a challenging task in the field of computer-aided *** important issue is to locate the area of lesion *** a branch of Convolutional Neural Networks(CNNs),3D Context-Enhanced(3DCE) frameworks are designed to detect lesions on CT *** False Positives(FPs) detected in 3DCE frameworks are usually caused by inaccurate region proposals,which slow down the inference *** solve the above problems,a new method is proposed,a dimension-decomposition region proposal network is integrated into 3DCE framework to improve the location accuracy in lesion *** the restriction of "anchors" on ratios and scales,anchors are decomposed to independent "anchor strings".Anchor segments are dynamically combined in accordance with probability,and anchor strings with different lengths dynamically compose bounding *** show that the accurate region proposals generated by our model promote the sensitivity of FPs and spend less inference time compared with the current methods.

关键词： lesion detection Computed Tomography(CT) dimension-decomposition 3D context computer-aided diagnosis

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An Early Depression Detection Model on Social Media using Emotional and Causal Features

An Early Depression Detection Model on Social Media using Em...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zong, Linlin Zheng, Jian Zhang, Xianchao Liu, Xinyue Liang, Wenxin Xu, Bo Dalian University of Technology School of Software Dalian China Dalian University of Technology School of Computer Science and Technology Dalian China

ISBN: (纸本)9798350337488

Early depression detection is essential to enable healthcare professionals to effectively intervene and treat the depressive conditions. In existing research, an increasing number of psychological manifestations of depression are incorporated, and demonstrated effective by integrating them into computational models, particularly the emotional cues of depression. Although emotional factors are effective in depression detection, few studies have paid attention to the underlying depressive causal factors hidden within social media text for depression detection. In this paper, we propose a novel partitioned filtering network model to extract emotional and causal features for predicting the depressive users on social media. Experimental results demonstrate the proposed model achieves superior performance over recent baseline models on the dataset by highlighting the emotional and causal factors. © 2023 IEEE.

关键词： Deep Learning Early Depression Detection Natural Language Processing Social media

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